A simple mortality risk prediction score for viper envenoming in India (VENOMS): A model development and validation study.

BACKGROUND: Snakebite is a neglected problem with a high mortality in India. There are no simple clinical prognostic tools which can predict mortality in viper envenomings. We aimed to develop and validate a mortality-risk prediction score for patients of viper envenoming from Southern India.
METHODS: We used clinical predictors from a prospective cohort of 248 patients with syndromic diagnosis of viper envenoming and had a positive 20-minute whole blood clotting test (WBCT 20) from a tertiary-care hospital in Puducherry, India. We applied multivariable logistic regression with backward elimination approach. External validation of this score was done among 140 patients from the same centre and its performance was assessed with concordance statistic and calibration plots.
FINDINGS: The final model termed VENOMS from the term "Viper ENvenOming Mortality Score included 7 admission clinical parameters (recorded in the first 48 hours after bite): presence of overt bleeding manifestations, presence of capillary leak syndrome, haemoglobin <10 g/dL, bite to antivenom administration time > 6.5 h, systolic blood pressure < 100 mm Hg, urine output <20 mL/h in 24 h and female gender. The lowest possible VENOMS score of 0 predicted an in-hospital mortality risk of 0.06% while highest score of 12 predicted a mortality of 99.1%. The model had a concordance statistic of 0·86 (95% CI 0·79-0·94) in the validation cohort. Calibration plots indicated good agreement of predicted and observed outcomes.
CONCLUSIONS: The VENOMS score is a good predictor of the mortality in viper envenoming in southern India where Russell's viper envenoming burden is high. The score may have potential applications in triaging patients and guiding management after further validation.

Introduction

Snakebite envenoming is a serious but neglected problem in the tropics [1,2]. South Asia, particularly, India has the largest burden of snakebite deaths and disabilities in the world [3,4]. Recent estimates suggest that annual mortality from snakebite envenomings in India is approximately 58,000; which is more than half the estimated global snakebite mortality. Thrice as many endure lifelong disabilities due to long-term consequences [4,5]. Affected are usually young adults belonging to lower socio-economic background who experience subsequent social stigma and discrimination [4,6,7]. In South Asia, the snake species under the epithet of “Big 4” i.e. Daboia russelii, Echis carinatus, Bungarus caeruleus and Naja naja garner widespread attention while the other regionally important snake species are also emerging as medically important [8,9]. The currently available polyvalent antivenom in India neutralizes venom of only these four species [10].

In clinical settings, snakebite envenoming syndromes are broadly categorized as neurotoxic and haemo-vasculotoxic. Neurotoxic symptoms are usually due to elapid bites i.e., cobra and krait, and vasculotoxic envenomings due to vipers. An important bedside test in establishing the diagnosis of viper envenoming is the whole blood clotting test (WBCT20) [11]. Two ml of freshly sampled venous blood in a dry, glass vessel or tube and left undisturbed for 20 minutes at ambient temperature. The vessel is tipped once, if the blood is still liquid (unclotted) and runs out, the patient is inferred to have hypofibrinogenaemia (“incoagulable blood”) as a result of venom-induced consumption coagulopathy (VICC) [11].

Among the viper species, Russell’s viper is widely distributed throughout Indian subcontinent including Sri Lanka and Myanmar [12,13]. A nationwide study and evidence synthesis estimated that 43% of reported bites in India are likely to be due to Russell’s viper envenoming [4]. Russell’s viper is reported as the species responsible for up to 80% mortality in several hospital-based series across India [14,15]. Variation in venom composition between Russell’s viper species from various parts of India leading to marked difference in neutralizing capability of the polyvalent antivenom has been recently demonstrated [12].

Russell’s viper envenoming is clinically complex and challenging as it results in a rapidly progressive multisystem dysfunction culminating in mortality. The envenoming haemo-vasculotoxic syndrome affects platelets, coagulation factors (like factors V and X), endothelium of the vessel wall resulting in VICC, thrombotic microangiopathy and capillary leak syndrome (CLS) [16,17]. VICC presents with bleeding manifestations which can range from mild bleeding like gum bleeds and bite-site bleeding to life threatening bleeds such as intracranial haemorrhage and gastrointestinal bleeds [18]. CLS has been reported from Russell’s viper bites in Southern India, Sri Lanka and Myanmar and is associated with a poor outcome [10,19]. CLS presents with manifestations of parotid swelling, conjunctival-chemosis, periorbital edema, hypotension, albuminuria and hemo-concentration. Other organ-systems including kidneys, heart, presynaptic neuromuscular junction and hypothalamo-pituitary axis are also affected in Russell’s viper envenoming resulting in acute kidney injury (AKI), early neuromuscular paralysis, acute adrenal insufficiency and long-term consequences like chronic kidney disease and Sheehan like syndrome [20-24].

The other important viper species with widespread distribution is Echis carinatus which has two subspecies: Echis carinatus accounts for for envenomings in the Indian peninsula while Echis carinatus sochureki is thought to be responsible for bites in Northern India and Pakistan [25,26]. Echis envenoming presents with local swelling, coagulopathy and bleeding manifestations. Apart from this several pit vipers such as hump-nosed pit viper (Hypnale hypnale), Himalayan and bamboo pit vipers in north-eastern India and Malabar pit viper in the western coast are also clinically significant [10]. Hump nosed pit viper can cause local necrosis, coagulopathy, bleeding and acute kidney injury and maybe misidentified as saw-scaled viper [27,28]. Syndromic diagnosis is widely applied especially in primary care settings despite its limitations in the absence of reliable species identification methods in routine clinical practice [29,30].

Managing viper bites is complicated involving multiple decisions like need for renal replacement therapy, ventilatory support for pulmonary edema, ionotropic support for distributive shock in capillary leak syndrome and transfusion support based on which organ systems are involved and when [31]. This is supported by several studies which report higher mortality and morbidity in viper envenoming [4,32]. Thus, viper envenomings have a complex pathogenesis with distinct prognostic factors involved implying that they merit the need for a distinct clinical decision support tool from elapid envenoming.

Recently, World health organization (WHO) has evolved a strategy to halve the snakebite mortality by 2030 as compared to 2015. One of the strategies in this call to action includes development of clinical decision support tools for improving outcomes [33]. Though several clinical parameters have been explored as mortality risk predictors in hospital-based studies, no simple score exists to quantify the prognostic factors affecting outcomes [34,35].

We aimed to develop and externally validate a simple, point-of-care mortality risk prediction score for patients presenting with syndromically diagnosed hemotoxic viper envenoming patients which could be potentially applied across healthcare settings.

Methods

Ethics statement

Both studies were approved by institutional ethics committees (JIPMER Institue Ethics Committee, JIP/IEC/SC/3/2012/13 and JIP/IEC/2014/1/24). Written informed consent was obtained at the time of data collection from the participant or parent/guardian along with participant’s assent if the participant’s age <18 years. However, repeat consent was not obtained, as this was a retrospective study using de-identified patient data from previous studies.

Study setting, populations, and design cohorts: The study site for both derivation and validation cohorts was a tertiary care referral hospital situated in Puducherry, India, located in the eastern coast of India. Our hospital has a catchment area of approximately 17,000 km2 wherein 8 medically important snakes including the “big 4” have been routinely reported [36]. (Fig 1). Russell’s viper is common and saw-scaled viper is routinely reported while pit vipers have not been reported in the area.

Fig 1

Map of India with state of Tamil Nadu.

The union territory of Puducherry (town), showing location of the study site with highlighted adjacent districts of the state of Tamil Nadu from where patients were enrolled. (Map not to scale. Maps created using https://www.datawrapper.de/).

Derivation cohort

We developed the model using data from a prospective cohort study of consecutive patients presenting to the emergency department of a tertiary care referral centre in Puducherry, India between September 2011 to August 2013.The clinical characteristics and outcomes of this prospective derivation cohort (n = 248) have been published previously [37]. Those patients ≥ 12 years of age, presenting with a history of snakebite or unknown bite with positive whole blood clotting test (WBCT20) and diagnosis of viper envenoming made by syndromic approach or identification of dead snake/photograph of the snake if brought by the patient were included. Syndromic diagnosis of viper envenoming was made based on syndromes 1, 2 and 5 in World Health Organization (WHO) guidelines. Syndrome 1 (All viperidae): Local envenoming (swelling) with bleeding/clotting disturbances. Syndrome 2: (Russell’s viper in South India/Myanmar/Sri Lanka): Local envenoming and bleeding/clotting disturbances with shock, acute kidney injury, conjunctival chemosis, acute pituitary insufficiency, ptosis, external ophthalmoplegia, facial paralysis or dark brown urine. Syndrome 5 (Russell’s viper in Sri Lanka or South India): Bitten on land and paralysis with dark brown urine/acute kidney injury with bleeding/clotting disturbances. Those with isolated neurotoxicity and local manifestations alone with normal WBCT20 were excluded (Syndromes 3 and 4) [31]. All patients in this cohort presented within 48 hours of bite while 67% presented within 6 hours of bite.

Validation cohort

We validated the model in an external cohort of 140 patients who presented to the same centre from September 2013 to July 2015.This cohort was comprised of patients from a randomized clinical trial investigating two different doses of polyvalent antivenom [38]. This cohort included patients who had abnormal WBCT20 and syndromic diagnosis of viper envenoming. However, this cohort excluded those who had received greater than 200 mL (20 vials) antivenom prior to presentation (trial registered at CTRI/2015/05/005826). All patients in this cohort also presented within 48 hours of bite.

Predictor variable selection

We searched for predictors of mortality in haemotoxic viper bite envenoming that were reported in previous studies or reviews (Table A in S1 Appendix -). We selected parameters that could easily be ascertained in different clinical settings with minimal interobserver variability and were part of the routine assessment in snakebite envenoming especially in primary care settings. Coagulation tests such as prothrombin time (PT), activated partial thromboplastin time (aPTT), serum fibrinogen, D-dimer were deliberately omitted considering the poor availability of these tests as point-of-care in primary care rural settings in India. For the purpose of this study clinical parameters assessed at 24 hours of admission, were defined as follows: a) signs of capillary leak syndrome (CLS) was defined as the presence of clinical evidence of at least one of the following: conjunctival chemosis, parotid swelling or periorbital puffiness with clinical evidence of pleural effusion or ascites b) overt bleeding: presence of bleeding from oral cavity, persistent bleeding from bite site hematuria, epistaxis, bleeding from intravenous puncture sites, hematemesis or melena, fresh bleeding per rectum, abnormal uterine bleeding or intracranial hemorrhage. c) renal dysfunction: Arbitrarily defined as serum creatinine > 3.0 mg/dl.) d) severe local envenoming: swelling involving more than one half of the bitten limb and bites involving the face/trunk. Urine output was measured over first 24 hours of admission and later converted to ml/hour.

Receiver operator characteristics (ROC) curves were constructed for each of the continuous variables from the derivation cohort to determine appropriate cut-offs to categorize them into clinically significant categories (Table B in S1 Appendix). Categorization of continuous variables was done in order to simplify the final score. For identifying additional predictors, we performed univariable (unadjusted) logistic regression analysis for each of identified risk factors and few others as dependent variables with mortality as outcome and we included significant (p < 0·05) predictors for model development (Table C in S1 Appendix). Sample size estimation was done using a thumb rule of 10 events per predictor [39]. As there were 57 events in the derivation cohort, the ideal number for predictors in the model was taken to be 6 to 7. Multiple imputation analysis was planned for addressing missing data if missing data for any predictor>5%.

Model development

The predictors finally selected for the multivariable model are enumerated in Table C in S1 Appendix. All candidate variables from the derivation cohort were entered into the multivariable logistic regression analysis. We used a backward stepwise elimination approach with the least statistically significant variable removed at each step. A total of five elimination steps simplified the model based on minimum Akaike Information Criteria (AIC) value.

Conversion to score

In the final model, we assigned the scores proportional to their β regression coefficients of the multivariable regression equation, using standard approach [40]. The variable with minimum β value was assigned a score of 1 and the remaining variables were assigned proportional scores with rounding off to the nearest integer to generate an easily calculable score [39,40]. An arbitrary cut-off score was chosen based on the score-mortality estimate graph.

Model performance, predictive accuracy, and external validation

Discrimination (i.e., the degree to which a model differentiates between those who died and survived) was calculated with concordance (c-index or statistic), equivalent to the area under the ROC curve. A value of 0.5 indicates no predictive ability, 0.8 is considered good, while 1 is perfect discrimination. Hosmer and Lemeshow goodness of fit statistic and Nagelkerke r2 were calculated for assessing overall model performance. To assess the calibration of the model, (i.e., agreement between predicted and observed risk of mortality), calibration plots were used. Perfect calibration is implied by a 45° diagonal line (calibration slope = 1 and a calibration intercept = 0). Deviations above or below the line reflects underprediction and overprediction by the model. We assessed the predictive accuracy of the score in the validation cohort with discrimination and calibration as mentioned above. We did all analysis with SPSS statistical software v23. Calibration plots were constructed Stata/IC v16 (trial version). The present study is reported in compliance with standard TRIPOD guidelines for prediction models (S1 TRIPOD Checklist).

Results

For the selection of candidate variables, 15 studies were reviewed to generate a list of 25 potential parameters. Related parameters were combined for clarity (e.g., shock and hypotension, anaemia, and haemoglobin < 10 g/dL). Ten parameters were considered infeasible for primary care settings and were excluded, among which, 3 were not deemed suitable for measurement on day 1 of bite. Two parameters reported in only a single study done on children were also not included (Table A in S1 Appendix). The derivation cohort included 248 while the validation cohort comprised 140 participants. Baseline characteristics for both cohorts are summarized in Table 1.

Table 1

Clinical characteristics of Derivation and Validation cohorts.

Characteristics	Derivation cohort (N = 248)	Validation Cohort (N = 140)
Enrolment period	August 2011—August 2013	September 2013—July 2015
Mean Age (SD) in years	40 (13–76)	39 (12–67)
Male gender (%)	168 (68)	103 (74)
Species identification:Snake species identified by dead snake or photographRussell’s viperSaw scaled viper	36 (14.5)17 (6.85)	8 (5.7)3 (2.1)
Syndromic diagnosisRussell’s Viper (Syndromes 2/5)Viperidae (Syndrome 1)	148 (59.7)47 (18.9)	108 (77.1)21 (14.8)
Lower limb bites (%)	206 (83)	119 (85)
Occupational bites (Agricultural activities) (%)	173 (70)	105 (75)
Antivenom dose (ml)—Median (IQR)	310 (167–420)	200 (100–290)
Bite to antivenom (h)—Mean (SD)	6.0 (3–12)	3.25 (2–6)
Acute Kidney Injury (%)	159 (64.1)	79 (56.4)
Required renal replacement therapy (%)	100 (40.3)	45 (32.1)
Required surgical limb debridement (%)	19 (7.6)	9 (6.4)
Mortality (%)	57 (22.9)	20 (14.3)

In the derivation cohort, 74.1% (n = 184) and validation cohort, 79.2% (n = 119) were classified as Russell’s viper envenoming by either snake identification or syndromic diagnosis (syndromes 2 & 5). Also, 19% in derivation cohort and 15% in validation cohort were classified as viper envenoming with unspecified species—syndrome 1 i.e., local swelling with prolonged WBCT20. A section of these patients is also expected to be Russell’s viper envenoming.

Univariable analysis in the derivation cohort (Table B in S1 Appendix,) found a significant association of in-hospital mortality with several predictors that were consistently reported previously: systolic blood pressure <100 mm Hg, presence of signs of capillary leak syndrome (CLS), any overt bleeding manifestations at admission, severity of local swelling, bite-to-antivenom time> 6.5h, haemoglobin <10 g/dL, presence of acute kidney injury (defined as creatinine >3 mg/dL), urine output < 20 mL/hour in the first 24 hours (measured over 24 hours), urine albumin positive by dipstick and thrombocytopenia (platelet < 260 x 109/L) (Table 2). These variables were entered into a multivariable model. Age and gender were also included in the model, despite being non-significant in the univariable analysis, because they were clinically relevant predictors.

Table 2

Variables in the final multivariable regression model at step 5 of backward elimination with regression coefficients, adjusted odds ratio, p value, confidence intervals and points allotted in the score.

Parameter	β	Adjusted Odds RatioExp (B)	P value(95% CI)	Points allotted in VENOMS score
Female Gender	0.903	2.467	0.084(0.89–6.87)	1
CLS	2.178	8.833	< 0.0001(3.33–23.44)	2
Bite to ASV >6.5 hours	0.660	1.934	0.109(0.74–5.08)	1
Bleeding	2.848	17.256	< 0.0001(3.84–77.57)	3
Haemoglobin < 10g/dL	0.806	2.238	0.108(0.84–6.10)	1
Urine output < 20 ml/h	2.173	8.783	< 0.0001(2.84–27.15)	2
SBP < 100	1.888	6.589	< 0.0001(2.44–17.77)	2
Constant	-7.276	0.001	< 0.0001

Bite to ASV: Bite to antivenom time >6.5 hours, CLS: Capillary leak syndrome, Hb: Haemoglobin < 10g/dL, SBP <100: Systolic Blood Pressure <100 mm Hg, Urine output < 20 ml/h on day 1 of admission.

Seven predictors remained in the multivariable model at step 5: overt bleeding, haemoglobin at admission <10 g/dL, bite to antivenom time> 6.5 hours, systolic blood pressure at admission < 100 mm Hg, presence of signs of capillary leak syndrome, urine output < 20 mL/hour in the first 24 hours and female gender (Tables 3 and 4). The predictors which were not significant at step 5 were also retained in the model considering optimal AIC and need to retain some clinically important predictors like bite-to-antivenom time which clinicians find valuable. Although AIC was minimum in step 6, we limited to five elimination steps in order to retain bite-to-antivenom time a clinically significant predictor variable as mentioned above based on clinician inputs and prior reports[34]. (Table 2 and Tables C and D in S1 Appendix). The regression equation and intercept (baseline mortality risk) are shown in Table 4. We assigned point values to these items and developed an integer-based estimation system (Tables 2 and 3).

Table 3

Calculation of VENOMS score.

Parameter	VENOMS score points
GenderFemaleMale	10
CLSYesNo	20
Bite to ASV time > 6.5 hoursYesNo	10
BleedingYesNo	30
Haemoglobin> 10 g/dL< 10 g/dL	10
Urine output (in first 24 hours)< 20 ml/hr> 20 ml/hr	20
Systolic BP< 100 mm Hg> 100 mm Hg	20

To calculate an individual’s VENOMS score, the points associated with each predictor can be added to obtain the total risk score. As an example, a female who has a presented 8 hours after snakebite with overt bleeding, Blood Pressure 120/80 mm Hg, with no signs of CLS and urine output of 10 ml/hr will have a risk score of 1 + 1 + 3 + 0 + 0 + 1 = 7 points. According to Fig 2, 7 points corresponds to a mortality risk of 22%. ASV: antivenom, BP: Blood pressure, CLS = Capillary Leak Syndrome.

Table 4

Final model with regression equation, intercept, and regression coefficients.

Log(p/1-p) = -7.276 + 0.903x1+ 2.178x2+ 0.660x3+ 2.848x4+ 0.806x5+ 2.173x6+1.888x6
Log(p/1-p) = Log odds of mortality, Constant = -7.276, X1: Female gender, X2: Signs of increased capillary permeability, X3: Bite to antivenom time > 6.5 hours, X4: Overt bleeding, X5: Haemoglobin < 10 g/dL, X6: Systolic BP < 100 mm Hg, X7: Urine output < 20 ml/hour.

Fig 2

A: Mortality risk plotted against each point of the score for the derivation cohort (n = 248) showing a sigmoid curve with steep increase in mortality at score was greater than 6. B: Mortality prediction estimates for validation cohort (n = 140).

Missing data

Missing data was < 5% for the predictor variables as data collection was prospective in the derivation cohort. Of the relevant predictors, data were 99·1% complete for 2 predictors (haemoglobin, platelet count) and 97.8% for serum creatinine. Data were complete for 100% of outcome parameters in the derivation cohort. Data was 100% complete for predictors and outcomes in the validation cohort as it was a randomized trial. As missing data was <5% we did not perform multiple imputation analysis.

Internal validation, discrimination, and calibration

Mortality risk plotted against each point of the score showed a sigmoid curve with steep increase in mortality when score was greater than 6 (Fig 2A). Hence, we decided to take a score of 6 as a cut-off for poor prognosis. Model discrimination using a ROC showed Area Under Curve (AUC/c-index) of 0.948 (95% CI 0.92–0.98) suggesting excellent discrimination. A cut-off of 6 as discussed above had a sensitivity of 90% and specificity of 83% for predicting mortality (Fig 3A). Hosmer-Lemeshow goodness of fit showed a chi-squared statistic of 1.52 (p = 0.99, df = 8) suggesting a good model fit. Nagelkerke r2 at step 5 was 0.69 again suggesting that the model explained 70% of the variability in the outcome parameter and a good overall performance (Table D in S1 Appendix). Internal calibration showed a slope of 1, intercept of 0 and an AUC of 0.95 suggesting excellent calibration in the derivation dataset (Fig A in S1 Appendix).

Fig 3

A: Model discrimination in derivation cohort using a receiver operator characteristic curve (ROC) showing area Under Curve (AUC/c-index) of 0.948 (95% CI 0.920–0.976). A cut-off of 6 had a sensitivity of 90% and specificity of 83% for predicting mortality. B: Model performance in validation cohort using a ROC showing AUC/c-index of 0·90 (95% CI 0·85–0·97).

External validation

The score was a significant predictor of mortality in the validation cohort (Odds ratio [OR] 1·8 per unit increase in score, 95% CI; p < 0·0001). Model performance in the validation cohort showed a c-statistic of 0·90 (95% CI 0·85–0·97) (Fig 2B). The model predicted a mean probability of mortality as 11% (95% CI 8–15%) in the validation cohort. Thus the 95% CI included the actually observed mortality of 14.3% indicating that calibration at large was satisfactory. Calibration plots of predicted and observed mortality showed a slope of 0.7, intercept of 0.4 and a c-index (AUC) of 0.92 suggesting overall overfitting of the model within the validation cohort with overprediction at low-risk patients and underprediction of mortality in high-risk patients (Fig 4). Prediction estimates in validation cohort are shown in Fig 2B. In the validation cohort, the lowest score of 0 predicted a mortality risk of 0.06% while a score of 12

Fig 4

Predicted versus observed mortality risk in the validation cohort.

Calibration plots showing a slope of 0.7, intercept (CITL) of 0.4 and a c-index (AUC) of 0.92. E:O: ratio of expected to observed mortality. Graph created using pmcalplot in STATA, Stata/IC 16 for Windows.

predicted a mortality of 99.1%. Sensitivity, specificity positive and negative predictive values (PPV and NPV) at each point in the score was calculated for the validation cohort and is presented in Table 5. At the selected cut-off of 6 the sensitivity was 75%, specificity 88.3%, PPV 52% and NPV 96% in the validation cohort.

Table 5

Accuracy of VENOMS score in predicting mortality in the validation cohort of patients with viper envenomation (n = 140).

VENOMS Score cutoff	Total number of patients corresponding to the cutoff	Among total patients, number of patients who died	Accuracy of score cut-off in predicting mortality among viper envenomed patients
			Sensitivity (95% CI)	Specificity (95% CI)	Positive predictive value (95% CI)	Negative predictive value (95% CI)
≥ 0	140	20	100 (83.2–100)	0 (0–3.0)	14.3 (14.3–14.3)	-
≥ 1	110	20	100(83.2–100)	25.0 (17.6–33.7)	18.2 (16.7–19.8)	100 (100–100)
≥ 2	87	20	100(83.2–100)	44.2 (35.1–53.5)	23.0 (20.3–25.9)	100 (100–100)
≥ 3	70	19	95.0(75.1–99.9)	57.5 (48.2–66.5)	27.1 (22.8–32.0)	98.6 (91.0–99.8)
≥ 4	58	19	95.0(75.1–99.9)	67.5(58.4–75.6)	32.8 (27.0–39.1)	98.8 (92.– 99.8)
≥ 5	42	17	85.0(62.1–96.8)	79.2(70.8–86.0)	40.5(31.4–50.2)	96.9(91.7–98.9)
≥ 6	29	15	75.0 (50.9–91.3)	88.3 (81.2–93.5)	51.7 (38.1–65.1)	95.5 (90.8–97.9)
≥ 7	17	11	55.0(31.5–76.9)	95.0 (89.4–98.1)	64.7 (43.3–84.1)	92.7 (88.6–95.4)
≥ 8	12	9	45.0(23.1–68.5)	97.5 (92.9–99.5)	75.0 (47.0–91.0)	91.4 (87.8–94.1)
≥ 9	9	7	35.0(15.4–59.2)	98.3 (94.1–99.8)	77.8 (43.9–94.0)	90.1 (86.8–92.6)
≥ 10	5	4	20.0 (5.7–43.7)	99.2 (95.4–99.98)	80.0 (32.0–97.1)	88.2 (85.7–90.3)
≥ 11	2	2	10.0(1.2–31.7)	100 (97.0–100)	100 (100–100)	87.0 (85.2–88.5)
12	0	0	0 (0–16.8)	100 (97.0–100)	-	85.7 (85.7–85.7)

Mortality prediction estimates for validation cohort (n = 140).

Model performance in validation cohort using a ROC showing AUC/c-index of 0·90 (95% CI 0·85–0·97).

Discussion

Snakebite envenoming usually affects those living in rural areas and in poverty [1,2,6]. Ending this neglect requires a refocus of research efforts into various aspects of snakebite envenoming including prognostic models to help classify patients according to severity and help plan appropriate management.

In this study, we have developed a practical prognostic instrument to predict the risk of in-hospital mortality after viper envenoming. The VENOMS score calculated on the day of admission was successfully externally validated and showed good discrimination and reasonable calibration in the same settings. The model incorporates seven items: overt bleeding manifestations, presence of signs of capillary leak syndrome, systolic blood pressure <100 mm Hg, urine output < 20 mL/h over first 24 hours (assessed over 24 hours), haemoglobin <10 g/dL, female gender, and bite to ASV time >6.5 hours. We prudently selected a list of candidate predictors and categorized them in the derivation cohort. Such a process involves making compromises, such as the exclusion of parameters that are not routinely assessed in a primary care clinical setting or that are not supported by sufficient validation data. The derivation cohort was adequately powered to show a good discrimination of the model. This is indicated by the 95% CIs of concordance statistics, which exceeded 0.8 in this cohort. Development and validation of the score followed established TRIPOD recommendations [41].

Prognostic scores support and improve the clinical decision making process and impact care by empowering clinicians to make evidence based decisions thereby improving patient outcomes[39]. Classical examples include Wells score for predicting pulmonary embolism and CURB 65 or pneumonia severity index for community acquired pneumonia. Both these scores have gained widespread applicability and have resulted in impacting diagnosis and management of these conditions including reduction in mortality of admitted patients in emergency departments [42,43]. Limited clinical prediction scores are available for neglected tropical diseases [44] A commonly reported score for snakebites is the Snakebite Severity Score (SSS) which ranges from 0 to 23 and assesses respiratory, cardiovascular, hematologic, gastrointestinal, central nervous system and local wound to assign scores for each [45]. The SSS was originally evolved for evaluating dry bites and deciding if patient requires antivenom or not. SSS has been shown to limit antivenom and other resource utilization [46,47]. It has been used as a prognostic score for haemotoxic bites in Indian settings, but a formal validation is unavailable [48]. The SSS has several limitations: it combines both neurotoxic and hemotoxic manifestations, includes several laboratory results including PT, aPTT, serum fibrinogen which are usually not available at primary care settings and common elapid neurological signs like ptosis do not figure in the score [49]. Apart from the SSS, studies from Korea have used the International Society of Thrombosis and Haemostasis scoring system for disseminated intravascular coagulation to classify viper bite patients with VICC though prognostic implications were unclear [50,51]. Another prognostic score is the Zululand Severity Score developed in South Africa for determining whether the patient requires antivenom or surgical intervention [52]. A species-specific severity grading for Indian snakes was evolved by Kumar V et al and was reported in subsequent hospital based studies [53,54]. However, the score is complex, the basis for severity grading are unclear and its prognostic implications were not validated. Patient-Specific Functional Scale (PSFS), is a patient-reported outcome that is validated for assessing limb recovery from snakebite envenoming [55]. In summary, there exists a need for a simple bedside prognostic instrument which can help triage and appropriately manage viper envenoming patients.

The VENOMS score has several potential practical applications despite being currently validated in a single centre: it can be applied readily at the bedside by clinicians without any device to stratify viper envenoming patients. We expect that the score can help tailor care according to risk-class by triaging low and high mortality risk (score >6) patients who may require early intensive care. We hypothesize that the score might aid decision making for early transfers while reducing unnecessary referrals in primary care settings. We also suspect that the score has a potential to reduce antivenom overuse in the form of additional doses in patients with low VENOMS score (e.g., a cut-off < 4 have mortality of 1.5%) similar to the SSS [46]. However, further clinical studies are warranted to confirm these suggestions. Cost-effectiveness and acceptability of VENOMS score also need further research. Likewise, the study opens several interesting questions which need further exploration in clinical context such as what are appropriate measures to reduce mortality, in high-risk individuals (Score >6) and what is performance of the score as a guide to supportive care?

Our study has several important limitations. A syndromic approach to identifying the offending snake may have resulted in errors. The scoring system has only been validated in the same centre as the derivation cohort, where the common species is Russell’s viper (at least 74% patients in derivation cohort and 79% in validation cohort fitted into confirmed or syndromic diagnosis of Russell’s viper). The score requires independent external validation in other settings before widespread applicability. The performance of this score in settings where saw-scaled viper envenoming forms bulk of cases will need appropriate modification of the score. The scope of the score is limited to in-hospital mortality.

Clinical manifestations vary greatly across India and South Asia, and our sample is from a single site. Geographical intraspecific variations in Russell’s viper envenoming has been known to cause varied clinical manifestations [12]. For example, capillary leak syndrome due to Daboia russelii envenoming has been frequently reported from Southern India, Sri Lanka, and Myanmar while there are only few reports of this phenomenon in from other areas in the subcontinent [19]. Likewise, pre-synaptic neurotoxic features in Russell’s viper envenoming appear to have limited geographical distribution [23]. Therefore, apart from the spectrum effect in clinical prediction scores, the score requires further widespread geographical as well as domain validation specifically in primary care settings.

All predictors were converted to categorical variables for ease of use, this might have led to some loss of information. There were some differences in baseline characteristics of both the cohorts even though they were from the same centre. This difference could be attributed to differences in study design (prospective cohort vs randomized clinical trial) and inclusion and exclusion criteria. Specifically, the validation cohort excluded patients who had received > 20 vials antivenom prior to admissions. It is possible that some severely envenomed patients (who are likely to receive higher doses of antivenom upfront at primary care settings) were missed in the derivation cohort. Also, even though both cohorts received antivenom from the same manufacturer (Table 1), multiple batch numbers were used according to institutional supply which might have resulted in varying action due to batch to batch variation [56,57]. It is pertinent to note that the median antivenom dose used by the derivation cohort is 30 vials which is the recommended upper limit for Russell’s viper envenoming suggesting that many patients received more antivenom than recommended but did not respond as expected. Also, the results are only applicable to adults >12 years of age as we did not include children who may have different clinical predictors as suggested by previous studies.

Selection bias needs to be considered because both cohorts pre-selected people with severe envenoming and the population was a tertiary care referral centre [39]. Both cohorts used clinical syndromic approach to snake identification based on the current WHO guidelines while serum-based assays could have ascertained species-based diagnosis of viper envenoming. However, this approach mimics a real-life situation, including rural primary care scenarios, possibly making the model applicable in these practice settings. There was deviation from the perfect slope in validation calibration plot (Fig 4). These deviations were limited in scope and within the estimated 95% CI. Also, smoothing techniques used to estimate the observed probabilities of the outcome in relation to the predicted probabilities, i.e. the loess algorithm may have affected the graphical impression, considering that the derivation cohort is a smaller dataset [58].

In conclusion despite limitations, the VENOMS score appears to be an easy-to-use point of care clinical prediction score for mortality prediction for Russell’s viper envenoming in Southern India with potential widespread applications in various settings.

TRIPOD checklist for prediction model development.

(DOCX)

Click here for additional data file.

Search Strategy, Potential variables considered and references.

Table A: Publications screened for variable selection for model development Table B: (Supplementary Appendix 1): Area under the curve (AUC) for Receiver-operating curves (ROC) constructed for continuous predictor variables with mortality (or survival) as the state variable with confidence interval (CI), cut off chosen and sensitivity and specificity at chosen cut-off. Table C: Odds ratio with 95% CI for univariable Binary Logistic Regression (unadjusted) and subsequent multivariable logistic regression with backward elimination strategy (adjusted) to predict mortality as outcome. Table D: Multivariable logistic regression model with backward elimination at step 5 and step 7 Table E: Model summary showing -2 log likelihood, Cox and Snell’s R square, Nagelkerke R Square and Akaike Information criteria (AIC) shown for each step of backward elimination. Fig A: Perfect Internal calibration in derivation cohort (slope of 1, intercept of 0 and an AUC of 0.95). Graph created using pmcalplot in STATA, Stata/IC 16 for Windows.

(DOCX)

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Deidentified patient data for derivation cohort.

(PDF)

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Deidentified patient data for validation cohort.

(PDF)

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27 Jul 2021

Dear Dr. Maya Gopalakrishnan,

Thank you very much for submitting your manuscript "A simple mortality risk prediction score for viper envenoming in India (VENOMS): A model development and validation study" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews and the editor's comments (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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***********************

Editor's comments:

1. A syndromic approach to identifying the offending snake may well have resulted in errors.

2. Clinical manifestations vary greatly across India, and this sample of bite victims is from a single site.

3. The scoring system has only been validated in a single centre and that this was the same centre as the derivation cohort. The score requires independent external validation in other settings.

The authors should acknowledge these major limitations in the methodology, and the discussion and conclusions should take these limitations into account (be less dogmatic).

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: Abstract line 26 and and intro line 124 - is there conclusive evidence to support that mortality is higher amongst viperid envenomings? I would have thought neurotoxic elapdis would have a higher case fatality rate?

Author summary line 55-57 - it is too early to suggest the score can be used to inform clinical practice. It has only been validated in a single centre, and that was the same centre as the derivation cohort. Suggest reword to, 'may become' or 'shows promise'.

Methods line 155 - specify if patients were enrolled consecutively for the derivation cohort?

Methods Table 1 - Although this is based on previously published data, suggest moving this table to the results section.

Methods table 2 - suggest removing this table. All of these covariates with corresponding ORs and 95% CIs should be listed in Table 3 of the results.

Choice of covariates - it seems unusual not to include biting species as a covariate as this seems like an important predictor of outcome. Was this not used due to missing data - in which case specify this in the paper? If there is too much missing data, in those cases where the species was known it would be helpful to present the mortality rate by species. This will show if, for example, those with known Russell's envenoming have a higher mortality than those with known Echis envenoming.

Reviewer #2: -Are the objectives of the study clearly articulated with a clear testable hypothesis stated? Yes

-Is the study design appropriate to address the stated objectives? Yes - however methodological concern as outlined below.

-Is the population clearly described and appropriate for the hypothesis being tested? Some concern - See below

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested? - Yes

-Were correct statistical analysis used to support conclusions? - Yes

-Are there concerns about ethical or regulatory requirements being met? - No concerns

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Results Table 3 - Include all covariates that were entered into the logistic regression model. Include unadjusted (univariate) and adjusted (multivariate) ORs, 95% CIs and p values.

Results Table 3 - there is an error in the 'Hb 10' covariate. P value is <0.0001 yet 95% CI 0.84-6.10. If this covariate is a non-significant predictor I would suggest removing it from the scoring system.

Results Table 4 - I would disagree with including 'gender' and 'bite to ASV' as predictors in the model. The confidence intervals cross 1 and therefore it is not clear if they are associated with a higher or lower risk of death. One would expect the model to be slightly more accurate without them?

Results line 306 - was multiple imputation necessary when 97.8% of data was available. Was missing data 'missing at random?' Unless data was not missing at random and this influenced the model, I would suggest not doing multiple imputation. I'm not a statistician so happy to be corrected if there is something I am missing.

Results line 331 - request that the sensitivity, specificity, PPV and NPV is calculated for every score cut-off in the validation cohort and that this be included in an additional table. See Abouyannis et al 2011 Table 3 for an example (doi:10.1097/QAD.0b013e328349a414). PPV and NPV are particularly meaningful when applied to clinical decision making.

Reviewer #2: -Does the analysis presented match the analysis plan? Yes

-Are the results clearly and completely presented? Yes

-Are the figures (Tables, Images) of sufficient quality for clarity? In the most part. Figure 1 could be improved.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: More emphasis should be placed on the fact that this scoring system has only been validated in a single centre and that this was the same centre as the derivation cohort. Highlight that the tool requires independent external validation in other settings with a high burden of Russell's viper envenoming.

Reviewer #2: -Are the conclusions supported by the data presented? Some modification required

-Are the limitations of analysis clearly described? - Yes, these could be grouped together better, see comments below.

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study? Yes, but can be improved.

-Is public health relevance addressed? Yes

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Appendix S3 is difficult to interpret as it shows the raw output from the logistic regression.

Reviewer #2: Running title – This is misleading – needs to specify Indian Vipers.

Line 26 - Reported mortality is higher in viper envenoming compared to snakes

– please correct.

Author summary

Line 57-58 It can also aid better decision making with respect to antivenom administration and other supportive care. The findings of this study do not support this statement, VENOMS tool was not validated as to whether patients should be administered antivenom/repeated antivenom

Image quality of Figure 1 is poor.

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: Overall I think the topic area and the findings of this paper are highly important and certainly worthy of acceptance once the above changes have been made. Particularly in India, where mortality rates from envenoming are high, there is a great need for validated scoring systems that clincians can use to help inform their decisions. The major limitation is that this is single centre, and validated in the same centre, so generalisibility is not confirmed.

Reviewer #2: The authors should be congratulated for devising a severity scoring system using a systematic approach to development and validation for Viper envenoming in India. Some suggestions and concerns are outlined below.

Methodological concerns

Syndromic approach to identifying snake may have resulted in errors in inclusion/exclusion

Sampling was from one site only – we know that clinical manifestations vary greatly across India and Indian subcontinent.

Validation cohort selected limited patients with >200ml antivenom prior to presentation. This is likely to bias the patient selection to less severely envenomed patients (demonstrated in Table 1 with reduced complication rates between groups 1 and 2 and a reduced mortality rate for the validation group).

Smaller concerns

Authors need to define CLS and the time at which data points were collected – Was this at admission or at 24 hours? (Bearing in mind the median time to development of signs is 48 hours).

Results

Did the authors assess heart rate as a potential variable? I note this was not included in the list of 25 potential variables listed in the supplementary material.

After selection of the variables for inclusion in the VENOMS score – I would like to see a comparison of mortality risk curves for data collected at different time points. E.g on admission, admission +12 hours, Admission +24 hours, Admission + 36 hours, Admission + 48 hours. If the time points mortality risk scores are not statistically significant for each time point, then it supports the statement that the score can be calculated at any point between presentation and 48 hours after.

Discussion

I believe the paragraph from Lines 438-449 should be communicated early in the discussion.

Please expand on how use of the VENOMS severity score may improve snakebite management? Are there examples in the literature? How have clinical severity scoring for other conditions improved care (such as CURB65)?

As mentioned in comments, I believe the subjects outlined between Lines 396-412 are limitations of this study methodology and that practical prospective assessment of this assessment tool is needed from multiple study sites (external validation) to corroborate the study findings.

Data sharing

All individual patient data sets should be made readily available to meet PLoS NTD data sharing statement. This is not presently provided.

--------------------

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Reviewer #1: Yes: Michael Abouyannis

Reviewer #2: No

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11 Nov 2021

Submitted filename: Response to reviewers comments.docx

Click here for additional data file.

14 Dec 2021

Dear Dr Gopalakrishnan,

Thank you very much for submitting your manuscript "A simple mortality risk prediction score for viper envenoming in India (VENOMS): A model development and validation study" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

The reviewers both acknowledged the considerable effort undertaken in the revision submitted, and the resulting impact that this has had on the manuscript. Please attend to the minor revisions suggested by reviewer one (and the reference corrections indicated by reviewer two). We thank the authors for engaging productively in the review process.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Nicholas R Casewell

Associate Editor

PLOS Neglected Tropical Diseases

Janaka de Silva

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

The reviewers both acknowledged the considerable effort undertaken in the revision submitted, and the resulting impact that this has had on the manuscript. Following the completion of the minor revisions suggested by reviewer one (and the reference corrections indicated by reviewer two) this manuscript will be accepted for publication. I thank the authors for engaging to productively in the review process.

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: Yes

Reviewer #2: (No Response)

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Table 5 - I suggest making it clear that each row refers to a score cut-off. Perhaps rename 1st column as “Venom score cut-off” and add ‘≥’ in front of the 0-12. It is also a bit confusing that for sens/spec/ppv/npv the accuracy using the cut-off is used, whereas for number of patient and number of deaths the number with that specific score is used. Perhaps change column 2 and 3 to the number of patients with that cut-off (i.e., the first row (score cut-off ≥0) would be 140 patients and 20 deaths. Second row 110 patients and 20 deaths). It may be possible to do this a different way - it just needs to be more clear and consistent for the reader.

Table 5 - I think there may be errors in the values in this table. For score cut-off of ≥2 the sensitivity is 97.5%. But no deaths are missed at this cut-off so sensitivity should be 100%? With a score cut-off of ≥3 the sensitivity is 95% (seems correct as 1 of 20 deaths missed). Score cut-off of ≥4, 90% sensitivity but this should be 95% as still only one death missed? I haven’t checked spec/PPV and NPV but I would suggest double checking all of the values in Table 5.

Reviewer #2: (No Response)

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: (No Response)

Reviewer #2: (No Response)

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Typo with full stop mid sentence line 406

Reviewer #2: (No Response)

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: An excellent study that has adopted a data driven apporach to developing a risk score for predicting mortality outcome of people with viper envenoming in India. With minor changes, as highlighted above, I would suggest this study be accepted.

Reviewer #2: The authors should be congratulated on the considerable improvement to the manuscript, particularly in reference to its potential applicability to clinical use. I look forward to seeing it validated in a multi-centre prospective study!

Please double check all citations correspond to the relevant reference (for example citation 38 does not correspond to reference 38 - (a letter in reference to another article)) and ensure that the list of references are referenced in accordance with journal specifications.

--------------------

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Reviewer #1: Yes: Michael Abouyannis

Reviewer #2: No

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

References

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article's retracted status in the References list and also include a citation and full reference for the retraction notice.

13 Jan 2022

Submitted filename: Response to reviewers_PlosNTD_10012021.docx

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20 Jan 2022

Dear Dr Gopalakrishnan,

We are pleased to inform you that your manuscript 'A simple mortality risk prediction score for viper envenoming in India (VENOMS): A model development and validation study' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

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Janaka de Silva

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***********************************************************

Following the final minor changes made in response to the reviewers, the revised manuscript has now been accepted for publication.

15 Feb 2022

Dear Dr Gopalakrishnan,

We are delighted to inform you that your manuscript, "A simple mortality risk prediction score for viper envenoming in India (VENOMS): A model development and validation study," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

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