Clinical examination for diagnosing circulatory shock.

PURPOSE OF REVIEW: In the acute setting of circulatory shock, physicians largely depend on clinical examination and basic laboratory values. The daily use of clinical examination for diagnostic purposes contrasts sharp with the limited number of studies. We aim to provide an overview of the diagnostic accuracy of clinical examination in estimating circulatory shock reflected by an inadequate cardiac output (CO). RECENT
FINDINGS: Recent studies showed poor correlations between CO and mottling, capillary refill time or central-to-peripheral temperature gradients in univariable analyses. The accuracy of physicians to perform an educated guess of CO based on clinical examination lies around 50% and the accuracy for recognizing a low CO is similar. Studies that used predefined clinical profiles composed of several clinical examination signs show more reliable estimations of CO with accuracies ranging from 81 up to 100%.
SUMMARY: Single variables obtained by clinical examination should not be used when estimating CO. Physician's educated guesses of CO based on unstructured clinical examination are like the 'flip of a coin'. Structured clinical examination based on combined clinical signs shows the best accuracy. Future studies should focus on using a combination of signs in an unselected population, eventually to educate physicians in estimating CO by using predefined clinical profiles.

INTRODUCTION

Many critically ill patients suffer from circulatory shock, which places them at increased risks of multiorgan failure, long-term morbidity and mortality [1,2]. Combinations of clinical, hemodynamic and biochemical variables are recommended for diagnosing shock [3,4].

Daily use of clinical examination (in any patient) for diagnostic purposes contrasts with the limited number of studies, so that the level of evidence in the critically ill is considered best practice [4]. Much remains unknown about the value of clinical examination in diagnosing shock, reflected by an inadequate cardiac output (CO) or maldistribution of blood flow. More knowledge on this topic could assist physicians in the diagnostic process and guide interventions. Previous overviews have evaluated the value of physical examination in sepsis patients [5], cardiovascular patients [6▪▪] and in hemodynamically unstable patients for predicting fluid responsiveness [7]. We aim to provide an overview of the diagnostic test accuracy of clinical examination findings for estimating CO in critically ill patients.

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BACKGROUND

‘Clinical examination’ of the cardiovascular system has been performed for a long time. The first evaluations of heart rate by palpation of the arterial pulse rate date back as far as approximately 335–280 B.C. [8]. Around the second century A.D., physicians recognized the value of pulse rate in diagnosing diseases. Pulse quality and quantity were extensively evaluated and distinctions were made in pulse fullness, rate, rhythm and size [9]. However, it would still take hundreds of years before the clinical assessment of circulatory shock ‘had evolved’ into the way as it is conducted today. In 1941, Ebert et al.[10] elaborately described the complexity of symptoms seen in systemic and peripheral circulatory failure in septic shock patients. He encountered the same clinical picture that we still face today:

(..) All the patients studied presented a similar clinical picture. They were stuporous or comatose. The rectal temperatures ranged from 36.1 to 41.3 degrees Celsius. The skin was pale and often covered with perspiration. The extremities were cold, and this finding usually preceded the fall in arterial pressure. The skin of the body was usually warm, although in terminal stages it too became cool. The radial pulse was feeble or impalpable. The pulse rate was rapid. (..)

For years, clinical examination was considered the cornerstone for diagnosing shock. Reliance on examination declined when Swan et al.[11] introduced pulmonary artery catheterization (PAC) in 1970. PAC allowed a wide range of pressure and flow-based hemodynamic measurements, including variables such as pulmonary capillary wedge pressure, systemic vascular resistance and CO[12]. Several studies concluded that the use of PAC frequently resulted in change of therapy compared with clinical examination [13-18]. However, PAC remained controversial because of its invasiveness in the absence of any clinical benefit [19-22]. Today, PAC has largely been replaced by less-invasive methods for assessment of CO, ranging from echo to pulse pressure analysis devices [23-26].

Despite these technological improvements, clinical examination still holds a prominent position in diagnosing circulatory shock [4,27]. We aimed to provide an overview of the diagnostic accuracy of clinical examination for the assessment of circulatory shock measured by CO or cardiac index (CI). We only included studies that estimated CO using clinical examination based on a one-time snapshot. Physicians mostly use changes in clinical examination findings as proxy for changes in CO to guide their interventions. To evaluate the diagnostic accuracy of changes in clinical examination in relation to changes in CO was beyond the scope of this review. In this review, we were mainly interested which clinical examination findings may accommodate clinical needs, because in daily practice these snapshot measurements guide treatment decisions as triggers for interventions.

METHODS

A sensitive search strategy was used to identify eligible studies (Appendix 1, http://links.lww.com/COCC/A17). In addition, we used the snowball and citation search methods on the selected articles. We attempted to include all studies that provided results on clinical examination findings in relation to CO. We excluded prognostic studies. We separated studies that evaluated univariable associations from studies that used multivariable analyses. Varying statistical indices for describing diagnostic test accuracy as well as a varying prevalence of low CO were encountered, limiting interstudy comparison. Whenever available, we used likelihood ratios as the preferred modality to describe diagnostic accuracy. Likelihood ratios may provide valuable information on disease probability in an individual and do not change with pretest probability (i.e. the prevalence of disease) [28-30]. We calculated sensitivity, specificity, predictive values and likelihood ratios of clinical examination for the detection of low CO whenever possible.

RESULTS

Our search resulted in 8128 hits of which 28 publications were selected. An additional six publications were identified through snowballing. After selection, we included 34 publications in this overview.

UNIVARIABLE STUDIES

Thirteen studies evaluated univariable associations of clinical examination variables with CO, including skin temperature or temperature gradients (n = 8) [31-38], capillary refill time (CRT; n = 1) [39], temperature gradient and CRT (n = 1) [40], mottling (n = 1) [41], heart rate and mean arterial pressure (n = 1) [42] and central venous pressure (n = 1; Table 1) [31-43]. The method used for measuring CO varied, including, for example thermodilution with the PAC or Doppler wave with transesophageal or transthoracic echocardiography.

Table 1

Prediction of cardiac output using a single variable

					Results
Author, year	Patients	Population	Variables of interest	Measurement method	Nonsignificant	Significant
Peripheral temperature
Kaplan et al. 2001 [31]	264a	Surgical ICU patients	Temp, subjective: foot (‘cool’ or ‘warm’)	PAC, technique not mentioned	–	’Cool’ : CI = 2.9 ± 1.2 ’Warm’: CI = 4.3 ± 1.2
Schey et al. 2009 [32]	10a	Post cardiac surgery	Temp, subjective: foot: (‘cool’ or ‘cool-warm’ or ‘warm’)Temp, objective of foot	PAC, thermodilution	Tskin, objective: r = 0.11	’Cool’ : CO = 3.71 ’Cool-warm’: CO = 4.83 ’Warm’ : CO = 5.12
Joly et al. 1969 [33]	100	Circulatory shock	Temp, objective: toeΔT: toe – ambient (ΔTp-a)	Indicator dilution technique	–	Tskin objective: r = 0.71 ΔTp-a: r = 0.73
Woods et al. 1987 [34]	26a	Circulatory shock	ΔT: central – toe (ΔTc-p)	PAC, thermodilution	ΔTc-p: no correlation
Vincent et al. 1988 [35]	15a	Cardiogenic and septic shock	ΔT: toe – ambient (ΔTp-a)	PAC, thermodilution	ΔTp-a in septic shock: no correlation	ΔTp-a in cardiogenic shock: r = 0.63
Bailey et al. 1990b [40]	40a	Post cardiac surgery	ΔT: central – toe (ΔTc-p)	PAC, thermodilution	ΔTc-p day of operation: no correlation	ΔTc-p postoperative day 1: r = −0.60
Sommers et al. 1995 [36]	21a	Post cardiac surgery	Tskin, objective: axillary, groin, knee, ankle, toe	PAC, thermodilution	Tskin, objective: no correlation in any site	–
Boerma et al. 2008 [37]	35	Sepsis and septic shock	ΔT: central – foot (ΔTc-p)	TEE, Doppler wave	ΔTc-p: r = −0.15	–
Bourcier et al. 2016 [38]	103a	Sepsis and septic shock	ΔT: toe – ambient (ΔTp-a)	TTE, technique not mentioned	ΔTp-a: no correlation	–
Capillary refill time
Bailey et al. 1990b [40]	40a	Post cardiac surgery	CRT: site not mentioned	PAC, thermodilution	CRT: no correlation	–
Ait-Oufella et al. 2014 [39]	59	Septic shock	CRT: index finger	FloTrac, arterial pressure waveform analysis	CRT: no correlation	–
Skin mottling
Ait-Oufella et al. 2011 [41]	60	Septic shock	Mottling score: knee	TTE, Doppler wave	Mottling score: no correlation	–
Systemic hemodynamic variables
Wo et al. 1993 [42]	256a	Severe injury and critically ill postoperative	HR, MAP	PAC, thermodilution	HR: r = 0.27, r2 = 0.07, MAP: r = −0.01, r2 = 0.0001,	MAP during severe hypotension: r = 0.50, r2 = 0.25
Kuntscher et al. 2006 [43]	16a	Major burns	Central venous pressure	Thermal dye double indicator dilution	–	Central venous pressure: r = 0.40

a=repeated measurements in each patient.

b=same study population.

ΔTc-p, central-to-peripheral temperature gradient (°C); ΔTp-a, peripheral-to-ambient temperature gradient (°C); CI, cardiac index (l/min/m2); CO, cardiac output (l/min); CRT, capillary refill time (s); HR, heart rate (beats/min); MAP, mean arterial pressure (mmHg); PAC, pulmonary artery catheter; TEE, transoesophageal echocardiography; Temp, temperature (°C); TTE, transthoracic echocardiography.

Circulatory shock may lead to compensatory vasoconstriction of nonvital, peripheral tissues such as the skin. Peripheral perfusion can easily be evaluated by measurement of skin temperature, CRT and degree of skin mottling. Two studies demonstrated that a subjectively cool skin temperature was associated with a lower CO[31,32]. Studies evaluating the correlation between objective temperature measurements and CO showed conflicting results; some observed moderate correlations [33,35,40], whereas most observed no correlation [34-38]. Skin temperature measurement methods differ widely and are likely influenced by several factors: age, ambient temperature, hypothermia, peripheral vascular disease, vasopressors, pain and anxiety have all been proposed as influencing circumstances [44,45]. This may explain the conflicting results and may limit its usefulness for estimating CO in clinical practice. Several studies have emphasized the prognostic value of prolonged CRT and mottling of the skin [39,41,46-49], but only three studies have evaluated their associations with CO and found no relevant correlations [39-41].

Prospective studies on systemic hemodynamic variables showed that heart rate, mean arterial pressure and central venous pressure were not directly correlated to CO[42,43,50]. Only during episodes of deep hypotension, one study observed a moderate correlation between mean arterial pressure and CO[42]. These systemic hemodynamic variables seem to be poor indicators of CO, which supports the common conception that low blood pressure is a late sign of circulatory shock and should not be relied on for early diagnosis [4,51].

MULTIVARIABLE STUDIES

Twenty-one studies evaluated multivariable associations of clinical variables with CO. Because of the differing methods of estimating CO, we subdivided our results into studies that evaluated the capacity of physicians to estimate CO (n = 17; Table 2) [13–18,52–61,62▪▪] and studies that constructed clinical profiles based on multiple variables (n = 3) or a multivariable model (n = 1) to correlate clinical examination findings with CO (Table 3) [63-66]. Furthermore, we could calculate the diagnostic test accuracy for physician's estimation of low CO in nine studies (Table 2).

Table 2

Physician's capacity to estimate cardiac output based on clinical examination

			Variables of interest			Results
Author, year	Patients	Setting	Classification	Estimation based on	Measurement method	Estimation	Diagnostic accuracy for low CO (95% CI)
Connors et al. 1983 [13]	62a	ICU	CI categorical: < 2.5; 2.5–3.5; > 3.5	Clinical assessment, laboratory and X-ray	PAC, thermodilution	44% correct estimation	Sens 58% (45–68%); Spec 60% (48–71%)PPV 58% (49–65%); NPV 60% (52–67%)LR+ 1.43 (1.02–2.00); LR– 0.71 (0.51–0.98)
Eisenberg et al. 1984 [14]	97	ICU	CO categorical: < 4.5; 4.5–7.5; > 7.5	Not described	PAC, thermodilution	51% correct estimation	Sens 71% (54–85%); Spec 56% (43–69%)PPV 48% (39–57%); NPV 78% (66–86%)LR+ 1.64 (1.15–2.33); LR– 0.51 (0.29–0.89)
Tuchschmidt et al. 1987 [15]	35	ICU	CO continuous	Clinical assessment and X-ray	PAC, thermodilution	r = 0.72	–
Connors et al. 1990 [17]	461	ICU	CI dichotomous: < 2.2; ≥2.2CI continuous	Clinical assessment, laboratory, X-ray and ECG	PAC, thermodilution	64% correct estimationMean CI-difference in CI = 1.0 ± 0.9	Sens 49% (40–57%); Spec 70% (65–75%)PPV 43% (38–49%); NPV 74% (71–77%)LR+ 1.62 (1.28–2.05); LR– 0.73 (0.62–0.87)
Celoria et al. 1990b [16]	114	Surgical ICU	CO categorical: < 4; 4–8; > 8	Clinical assessment, laboratory and X-ray	PAC, thermodilution	51% correct estimationr = 0.47	Sens 67% (30–93%); Spec 80% (71–87%)PPV 22% (14–34%); NPV 97% (92–99%)LR+ 3.33 (1.83–6.07); LR– 0.42 (0.16–1.05)
Steingrub et al. 1991b [53]	152	Surgical and medical ICU	CO categorical: < 4; 4–8; > 8	Clinical assessment, laboratory and X-ray	PAC, thermodilution	51% correct estimation	Sens 54% (37–70%); Spec 73% (63–81%)PPV 40% (31–51%); NPV 82% (76–87%)LR+ 1.96 (1.29–2.98); LR– 0.64 (0.44–0.91)
Mimoz et al. 1994 [18]	112	ICU	Combinations of CI, PAOP and SVRI	Clinical assessment, laboratory, X-ray and echocardiography	PAC, thermodilution	56% correct estimation	–
Staudinger et al. 1998 [54]	149	ICU	CI categorical: < 2.0; 2.0–4.0; > 4.0	Clinical assessment, medical history, laboratory and X-ray	PAC, thermodilution	62% correct estimation	–
Rodriguez et al. 2000 [55]	33	ED + respiratory distress or hypotension	CI categorical: < 2.6; 2.6–4.0; > 4.0.	Clinical assessment, medical history, laboratory, X-ray and ECG	TEE, Doppler wave	κ1 = −0.04 (95% CI–0.31–0.24)κ2 = 0.07 (95% CI −0.17–0.31)	–
Linton et al. 2002 [56]	50	Post cardiac surgery	CI categorical: < 1.9; 1.9–3.5; > 3.5	Not described	LiDCO, indicator-dilution	54% correct estimation	Sens 42% (15–72%); Spec 74% (57–87%)PPV 33% (18–54%); NPV 80% (71–87%)LR+ 1.58 (0.67–3.72); LR– 0.79 (0.47–1.32)
Iregui et al. 2003 [57]	105	ICU	CI categorical: < 2.5; 2.5–4.5; > 4.5	Clinical assessment, laboratory and X-ray	TEE, Doppler wave	44% correct estimation	–
Veale et al. 2005 [58]	68	ICU	CI categorical: < 2.5; 2.5–4.2; > 4.5	Not described	BioZ CO monitor, Impedance cardiography	42% correct estimation	Sens 22% (6–48%); Spec 66% (51–79%)PPV 19% (8–38%); NPV 70% (63–76%)LR+ 0.65 (0.25–1.68); LR– 1.18 (0.86–1.62)
Rodriguez et al. 2006 [59]	31	ED + endotracheal intubation	CI categorical:ranges not specified	Clinical assessment, medical history, laboratory and X-ray	TEE, Doppler wave	κ = 0.57 (95% CI 0.36–0.77)	–
Nowak et al. 2011 [60]	38	ED + respiratory distress	CO categorical < 4.0; 4.0–8.0; > 8.0	Clinical assessment and medical history	Nexfin, ABP waveform analysis	50% correct estimation
						κ = −0.02 (95% CI −0.25–0.20)	Sens 33% (4–78%); Spec 63% (44–79%)
							PPV 14% (5–36%); NPV 83% (73–90%)
							LR+ 0.89 (0.26–3.00); LR– 1.07 (0.57–2.00)
Duan et al. 2014 [61]	132	ICU	CI categorical:<3; 3–5; >5	Not described	PiCCO, thermodilution	50% correct estimation	–
Perel et al. 2016 [62▪▪]	206a	ICU	CO continuous	Clinical assessment	PiCCO, thermodilution	Percentage error = 66%
						Absolute mean difference in CO = −1.5 ± 2.2	–

a=repeated measurements in each patient.

b=overlapping study populations.

95% CIs, 95% confidence intervals; CI, cardiac index (l/minute/m2); CO, cardiac output (l/min); ECG, electrocardiography; ICU, intensive care unit; LiDCO, lithium dilution cardiac output; LR–, negative likelihood ratio; LR+, positive likelihood ratio; NPV, negative predictive value; PAC, pulmonary artery catheter; PAOP, pulmonary artery occlusion pressure (mmHg); PiCCO, pulse contour cardiac output; PPV, positive predictive value; Sens, sensitivity; Spec, specificity; SVRI, systemic vascular resistance index (dynes · s/cm5 · min2); TEE, transesophageal echocardiography.

Table 3

Combined signs of clinical examination for estimation of CO

			Variables of interest
Author, year	Patients	Population	Clinical profile	Clinical profile based on	CO-measurement	Results
Combined clinical profiles
Ramo et al. 1970 [63]	98	AMI	I (normal CI): no signs of HFII (normal CI): mild-to-moderate HFIII (low CI): overt pulmonary edemaIV (low CI): cardiogenic shock	Mean arterial pressure, cool extremities, urine output, mental status, third heart sound gallop rhythm and rales	PAC, indicator-dilution technique	I (normal CI): 23 of 45 (51%)II (normal CI): 19 of 30 (63%)III (low CI): 10 of 10 (100%)IV (low CI): 13 of 13 (100%)
Forrester et al. 1977 [64]	200	AMI	I (normal CI): no pulmonary congestion or peripheral hypoperfusionII (normal CI): pulmonary congestion onlyIII (low CI): hypoperfusion onlyIV (low CI): both	Heart rate, blood pressure, cool extremities, urine output and mental status	PAC, thermodilution	Overall: 81% correct estimations of CII & II (normal CI): 84 of 95 (88%)III & IV (low CI): 76 of 105 (72%)
Grissom et al. 2009 [65]	405	ALI	I: All three clinical signs aberrantII: Any one clinical sign aberrant	Capillary refill time, knee mottling and cool extremities	PAC, thermodilution	92% correct estimations of CI in class I:Sens 12% (3–28%); Spec 98% (97–99%)PPV 40% (17–69%); NPV 93% (92–93%)LR+ 7.52 (2.23–25.3); LR– 0.89 (0.79–1.01)75% correct estimations of CI in class II:Sens 52% (34–69%); Spec 78% (73–82%)PPV 17% (12–23%); NPV 95% (93–96%)LR+ 2.31 (1.58–3.38); LR– 0.62 (0.44–0.89)
Multivariable analysis
Sasse et al. 1996 [66]	23a	ICU patients	CO continuous	Heart rate, respiratory rate, mean arterial pressure and temperature	PAC, thermodilution	Heart rate: R2 = 0.05Respiratory rate: R2 = 0.14Mean arterial pressure: R2 = 0.03

a=repeated measurements in each patient.

ALI, acute lung injury; AMI, acute myocardial infarction; CI, cardiac index (l/min/m2); CO, cardiac output (l/min); HF, heart failure; LR–, negative likelihood ratio; LR+, positive likelihood ratio; NPV, negative predictive value; PAC, pulmonary artery catheter; PPV, positive predictive value; Sens, sensitivity; Spec, specificity.

PHYSICIAN'S CAPACITY TO ESTIMATE CO BASED ON CLINICAL EXAMINATION

Seventeen studies evaluated the accuracy of physician's estimates or ‘educated guesses’ of CO as compared to objectively measured CO. Estimates were based on clinical examination, with or without knowledge of medical history, biochemical values and/or radiological imaging (Table 2). Some studies used a categorical variable for CO estimates (e.g. ‘low’, ‘normal’ or ‘high’), whereas others used a continuous scale (e.g. 1–12 l per min) [15,17,62▪▪]. Physician's estimates were correct in 42–62% of the time [13-18,52-61]. Moderate-to-reasonable correlations and a high percentage error were found when physician's estimates of continuous CO were compared to objectively measured CO[15,16,62▪▪]. Moderate-to-very poor agreements were found in studies that used weighted κ statistics to address agreement occurring by chance [55,59,60,67]. In addition, two studies reported that 21 and 26% of the CO estimations were completely disparate (an estimated high CO when the objective CO was low or vice versa) [55,59].

Nine studies provided enough data for calculation of the diagnostic accuracy of physician's estimates for detecting low CO. The overall results appeared disappointing [13,14,16,17,53,54,56,58,60] (Table 2). Furthermore, two studies concluded that physicians more frequently overestimated (31–33%) rather than underestimated (18–23%) CO[14,57], implicating that physicians were more prone to miss an insufficient CO. Perel et al.[62▪▪] found the opposite when physicians were asked to estimate CO on a continuous scale.

These results suggest that physicians are not very capable to subjectively estimate CO based on clinical examination. The widely varying diagnostic accuracies are probably the result of different populations or cutoffs for a low CO, but overall it seems that physician's estimates are ‘an inaccurate diagnostic test’. This is in accordance with two studies of Saugel et al.[67,68], which both demonstrate the incapability of physicians to reliably assess volume status using simple clinical signs. Furthermore, five out of six studies concluded that predictions of senior staff members were equally bad as those of residents or fellows [13,18,54,61,62▪▪,69]. Finally, one study found that the accuracy of estimates was unrelated to the level of confidence physicians had in their assessment [69].

Several important limitations apply. Many studies did not elaborate their methods of clinical examination in terms of variables used and definitions employed, leaving variability at the physician's discretion so that these studies cannot be reproduced. PAC was used in most studies, but only in selected patients who failed to respond to initial therapy or in whom clinical examination alone was deemed insufficient, so that evaluation of the accuracy of clinically estimated CO will be biased by definition. Likewise, many other studies also used convenience samples, which hampers generalizability of their results. Clinical examination should be performed in a standardized fashion, according to a protocol, to maximize interobserver agreement and generalizability.

Three studies have compared predefined clinical profiles based upon clinical examination with objectively measured CI (Table 3). Forrester et al.[64] found a good agreement in patients with acute myocardial infarction (AMI). In their study, 75% of patients with low CI and 96% of patients with very low CI had clinical signs of peripheral hypoperfusion, such as decreased skin temperature, confusion or oliguria in conjunction with either arterial hypotension or tachycardia. Ramo et al.[63] observed 100% correct estimation of low CI when patients with AMI had overt signs of pulmonary edema or signs of cardiogenic shock. In their study, clinical signs of overt pulmonary edema were defined by rales or a third heart sound gallop rhythm and cardiogenic shock was diagnosed by the presence of a systolic blood pressure below 90 mmHg, oliguria, cold extremities and disorientation. These findings suggest that physicians can diagnose cardiogenic shock in patients with AMI using clinical examination. Accurate estimation of CO for diagnosing shock in all critically ill patients based on clinical examination might appear much more difficult because of large interindividual differences. Grissom et al.[65] combined CRT, mottling and skin temperature to predict CI in an unselected cohort of patients with acute lung injury. The presence of all three physical signs had a high specificity (98%) but a low sensitivity (12%) for diagnosing shock, suggesting that these three signs accurately rule in, but inaccurately rule out circulatory shock. Varying types of shock are probably associated with varying clinical signs [70], so that a ‘one size fits all’ approach seems inappropriate. Roughly, one-third of all patients with circulatory shock suffer from a low CO, whereas two-thirds have distributive shock with associated high CO[1,71]. Especially in the latter, clinical examination may indicate inadequate circulation regardless of the height of CO and it is difficult to establish how much CO is sufficient for each individual patient.

PREDICTING CO USING A MULTIVARIABLE MODEL

One study used multivariable regression analyses to estimate CO based on heart rate, respiratory rate, mean arterial pressure and central temperature (Table 3) [66]. These multivariable results confirm that systemic hemodynamic variables do not correspond well with CO. Future diagnostic studies of CO should therefore incorporate all clinical and hemodynamic variables in a multivariable model.

CONCLUSION

Clinical examination findings are poorly associated with CO in single-variable and multivariable analyses. Physicians seem to be insufficiently capable to estimate CO or recognize a low CO using their clinical examination. The most promising results were found when CO was estimated by using predefined profiles composed of combined clinical examination signs. However, most studies were conducted in highly selected populations and the details of estimations were not specified. On the basis of current evidence, using clinical examination to diagnose CO can, to our opinion, not be considered best practice. Future studies on this topic should be conducted in a representative population, use standardized clinical examination and use appropriate statistical indices of diagnostic accuracy. Ultimately, these results should guide education of physicians to estimate CO using predefined clinical profiles.

Acknowledgements

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Conflicts of interest

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REFERENCES AND RECOMMENDED READING

Papers of particular interest, published within the annual period of review, have been highlighted as:

▪ of special interest

▪▪ of outstanding interest