| Literature DB >> 33936198 |
Cheng-Chi Lee1,2, Yu-Chi Wang1, Yu-Tse Liu1, Yin-Cheng Huang1,3, Peng-Wei Hsu1, Kuo-Chen Wei1, Ko-Ting Chen1, Ya-Jui Lin1, Chi-Cheng Chuang1.
Abstract
INTRODUCTION: Postoperative delayed hyponatremia is a complication associated with transsphenoidal pituitary surgery. Due to a wide spectrum of symptoms, the reported incidence and predictors of postoperative delayed hyponatremia vary among studies, and this deserves to be reviewed systematically.Entities:
Year: 2021 PMID: 33936198 PMCID: PMC8055398 DOI: 10.1155/2021/6659152
Source DB: PubMed Journal: Int J Endocrinol ISSN: 1687-8337 Impact factor: 3.257
Figure 1PRISMA flow diagram of study selection. The number of search hits corresponding to each step of the systematic literature search, qualitative review, and quantitative analysis is shown. The reasons for search hit exclusion are described.
Summary of demographics of selected studies.
| First author (year) | Study design | Country | No. of pts | Type of surgery | Male (%) | Age (year) | Tumour pathology | Macroadenoma vs. Microadenoma | NOS score |
|---|---|---|---|---|---|---|---|---|---|
| Rajaratnam (2020) | Retrospective | India | 222 | mTSS/eTSS | 63 | 45 | NFPA 100% | 100% vs. 0% | 6 |
| Schur (2020) | Retrospective | Canada | 84 | eTSS | 56 | 54.1 | N/A | 100% vs. 0% | 6 |
| Patel (2019) | Retrospective | USA | 367 | eTSS | 46 | 48.5 | NFPA 44%, PRL 13%, ACTH-secreting 13%, GH-secreting 4%, atypical 1%, RCC 10%, craniopharyngioma 4%, meningioma 3%, others 3% | 100% vs. 0% | 6 |
| Younus (2019) | Retrospective | USA | 584 | eTSS | 51 | N/A | NFPA 39%, acromegaly 21%, PRL 26%, Cushing 14% | N/A | 8 |
| Yoon (2019) | Retrospective | Korea | 234 | eTSS | 49 | 54.4 | NFPA 100% | N/A | 8 |
| Tomita (2019) | Retrospective | Japan | 107 | eTSS | 36 | 54 | NFPA 69%, GHoma 22%, Cushing 5%, PRLoma 4%, TSHoma 1% | N/A | 6 |
| Agam (2018) | Retrospective | USA | 1153 | mTSS/eTSS | 46 | 49.5 | NFPA 54.0%, GHoma 14.6%, PRLoma 14.1%, ACTHoma 12.2%, other 5.0% | N/A | 6 |
| Bohl (2018) | Retrospective | USA | 172 | mTSS/eTSS | 57 | 51.9 | NFPA 59.6%, PRLoma 14.9%, acromegaly 8.5%, Cushing 9.0%, TSHoma 0.5%, others 8.0% | N/A | 6 |
| Burke (2018) | Retrospective | USA | 788 | mTSS/eTSS | 45 | 47.7 | NFPA 20.3%, PRLoma 6.6%, Cushing 10.5%, acromegaly 12.3%, TSHoma 0.5%, RCC 8.9%, other 4.7% | N/A | 6 |
| Deaver (2018) | Retrospective | USA | 287 | mTSS/eTSS | 51 | 53 | NFPA: 15.7%, functioning: 82.9%, other: 1.4% | 85.7% vs. 14.3% | 6 |
| Hollon (2018) | Retrospective | USA | 400 | eTSS | 54 | 53.9 | NF 59.8%, acromegaly 22.8%, Cushing 13.0%, PRL 4.0%, TSHoma 0.5% | 84.7% vs. 15.3% | 8 |
| Krogh (2018) | Retrospective | UK | 314 | mTSS/eTSS | 49 | 53.1 | NFPA 40.8%, acromegaly 12.4%, Cushing 4.4%, PRL 4.5%, craniopharyngioma 6.4%, Rathke cleft cyst 6.1%, meningioma 3.2%, other 19.4% | 41% vs. 59% | 8 |
| Bohl (2016) | Retrospective | USA | 303 | mTSS/eTSS | 54 | 52.9 | NFPA 67.3%, acromegaly 9.9%, Cushing 7.9%, TSHoma 1.0%, PRLoma 11.9%, others 2% | N/A | 6 |
| Barber (2014) | Retrospective | USA | 344 | mTSS/eTSS | 45 | 48 | NFPA 66.3%, functional adenoma 16.0%, RCC 14.0% | N/A | 6 |
| Yamada (2014) | Retrospective | Japan | 90 | mTSS/eTSS | 48 | 42 | TSHoma 100% | 82% vs.18% | 6 |
| Hussain (2013) | Prospective | USA | 339 | mTSS/eTSS | 39 | 48 | NFPA 33%, Cushing 24%, PRLoma 10.5%, acromegaly 8.5%, RCC 2.9% | N/A | 6 |
| Mamelak (2013) | Retrospective | USA | 300 | eTSS | 43 | 51.6 | NF 50.7%, ACTHoma 4%, GHoma 13.7%, PRLoma 5.7%, RCC 10%, other 15.9% | N/A | 6 |
| Chen (2011) | Prospective | China | 385 | eTSS | 53 | 51 | NFPA 100% | 8% vs. 92% | 6 |
| Kinoshita (2011) | Retrospective | Japan | 88 | mTSS | 32 | 47.9 | NFPA 27%, GHoma 20.5%, PRLoma35.2%, FSH or LHoma 13.6%, TSHoma 3.4% | N/A | 6 |
| Zada (2010) | Retrospective | USA | 169 | eTSS | 54 | 44 | Acromegaly 100% | 61% vs. 39% | 6 |
| Lee (2008) | Retrospective | Korea | 94 | mTSS | 54 | 42.8 | NFPA 53%, PRLoma 31%, GHoma 12%, ACTHoma 4% | 10% vs. 90% | 6 |
| Zada (2007) | Retrospective | USA | 369 | eTSS | 46 | 48 | NFPA 50%, PRLoma 12%, GHoma 12%, Cushing 9%, RCC 9%, others 8% | 76% vs. 24% | 6 |
| Sata (2006) | Retrospective | Japan | 105 | mTSS/eTSS | 35 | 43 | NFPA 38%, acromegaly 25%, RCC 15%, PRLoma 12%, Cushing 2%, others 8% | 11% vs. 89% | 6 |
| Hensen (1999) | Retrospective | Germany | 1571 | mTSS | 43 | 44.5 | NFPA 34%, Cushing 15%, acromegaly 26%, PRLoma 22.8%, others 2.2% | 64% vs. 30% | 6 |
| Kelly (1995) | Retrospective | USA | 99 | mTSS | 32 | 45 | N/A | 35% vs. 64% | 6 |
| Taylor (1995) | Retrospective | USA | 2297 | mTSS | 36 | N/A | N/A | N/A | 6 |
| Sane (1994) | Prospective | Finland | 91 | mTSS | 48 | 45 | NFPA 29%, GHoma 26%, ACTHoma 20%, PRLoma 15%, gonadotropinomas 3%, others 7% | 35% vs. 65% | 6 |
ACTHoma, adrenocorticotropic hormone-secreting adenoma; eTSS, endoscopic transsphenoidal surgery; FSH, follicular-stimulating hormone; GHoma, growth hormone-secreting adenoma; LHoma, luteinizing hormone adenoma; mTSS, microscopic transsphenoidal surgery; N/A, not applicable; NFPA, nonfunctioning pituitary adenoma; NOS, Newcastle-Ottawa Scale; PRLoma, prolactinoma; RCC, Rathke's cleft cysts; TSHoma, thyroid-stimulating hormone-secreting adenoma.
The summary of outcomes.
| First author (year) | No of patient | Criteria of delayed hyponatremia | Delayed hyponatremia, | Symptomatic delayed hyponatremia, | As cause for unplanned readmission within 30 days |
|---|---|---|---|---|---|
| Rajaratnam (2020) | 222 | Serum sodium <135 mmol/L on or after POD 3 | 58 (26%) | 11 (5%) | N/A |
| Schur (2020) | 84 | Serum sodium <135 mmol/L, on or after POD 5 | 5 (6%) | N/A | N/A |
| Patel (2019) | 367 | Serum sodium <135 mmol/L with associated symptoms | 55 (15%) | 55 (15%) | 20/33 |
| Younus (2019) | 584 | Serum sodium <135 mmol/L under routine screening on POD 6, with associated symptoms | 16 (2.7%) | 16 (2.7%) | 16/27 |
| Yoon (2019) | 234 | Serum sodium <135 mmol/L on or after POD 3 | 53 (22.6%) | 5 (2.1%) | N/A |
| Tomita (2019) | 107 | Serum sodium <135 mmol/L on or after POD 3 | 25 (23.4%) | N/A | N/A |
| Agam (2018) | 1153 | Serum sodium <135 mmol/L with associated symptoms | 48 (4.2%) | 48 (4.2%) | N/A |
| Bohl (2018) | 172 | Serum sodium <135 mmol/L under screening | 29 (15%) | 11 (6%) | N/A |
| Burke (2018) | 788 | Serum sodium <135 mmol/L with associated symptoms | 20 (2.5%) | 20 (2.5%) | N/A |
| Deaver (2018) | 287 | Serum sodium <135 mmol/L with associated symptoms | 13 (4.5%) | 13 (4.5%) | 13/25 |
| Hollon (2018) | 400 | Serum sodium <135 mmol/L on or after POD 3 | 54 (14.3%) | N/A | N/A |
| Krogh (2018) | 314 | Serum sodium <130 mmol/L on POD 6–8 | 26 (8.3%) | 14 (4.46%) | 14/56 |
| Bohl (2016) | 303 | Serum sodium <135 mmol/L with associated symptoms | 15 (4.95%) | 15 (4.95%) | 15/27 |
| Barber (2014) | 344 | Serum sodium <135 mmol/L on or after POD 3 | 35 (10.2%) | N/A | N/A |
| Yamada (2014) | 90 | Serum sodium <135 mmol/L on or after POD 3 | 9 (10%) | N/A | N/A |
| Hussain (2013) | 339 | Serum sodium <130 mmol/L on POD 6–13 | 50 (15%) | 22 (6.4%) | N/A |
| Mamelak (2013) | 300 | Serum sodium <133 mmol/L on or after POD 3 | 37 (12.33%) | 8 (2.67%) | 8/38 |
| Chen (2011) | 385 | Serum sodium <135 mmol/L on or after POD 3 | 85 (22.1%) | N/A | N/A |
| Kinoshita (2011) | 88 | Serum sodium <135 mmol/L with associated symptoms | 27 (30.7%) | 9 (10.2%) | N/A |
| Zada (2010) | 169 | Serum sodium <135 mmol/L on or after POD 3 | 4 (2%) | N/A | N/A |
| Lee (2008) | 94 | Serum sodium <135 mmol/L on or after POD 3 | 17 (18%) | 7 (7.4%) | N/A |
| Zada (2007) | 369 | Serum sodium <135 mmol/L on or after POD 3 | 103 (28%) | 29 (7.9%) | N/A |
| Sata (2006) | 105 | Serum sodium <135 mmol/L on or after POD 3 | 24 (22%) | 4 (3.64%) | N/A |
| Hensen (1999) | 1571 | Serum sodium <132 mmol/L on or after POD 3 | 37 (2.4%) | 32 (2.1%) | N/A |
| Kelly (1995) | 99 | Serum sodium <135 mmol/L on or after POD 3 | 9 (9%) | 7 (7%) | N/A |
| Taylor (1995) | 2297 | Serum sodium <135 mmol/L with associated symptoms | 53 (2.3%) | 53 (2.3%) | N/A |
| Sane (1994) | 91 | Serum sodium <132 mmol/L on or after POD 3 | 32 (35%) | 18 (19.8%) | N/A |
POD, postoperative day; N/A, not applicable.
Figure 2Main results of meta-analysis: incidence and hospital readmissions. The values of individual studies and pooled estimates of (a) overall incidence of delayed hyponatremia, (b) incidence of symptomatic delayed hyponatremia, and (c) rate of hospital readmission within 30-day postoperatively due to delayed hyponatremia are shown. A random-effects model was adopted based on the results from the heterogeneity test.
Stratified meta-analysis of the incidence of delayed hyponatremia.
| Number of studies |
|
| Pooled event rate with 95% CI | |
|---|---|---|---|---|
| Type of surgery | ||||
| eTSS | 11 | 164.8 | 93.9 | 0.113 (0.075, 0.168) |
| mTSS | 7 | 280.2 | 97.7 | 0.092 (0.036, 0.218) |
|
| ||||
| Publication year | ||||
| Before 2011 | 10 | 471.3 | 98.1 | 0.121 (0.056, 0.241) |
| 2011+ | 17 | 269.6 | 94.1 | 0.097 (0.068, 0.135) |
|
| ||||
| Mean age | ||||
| <50 years | 15 | 433.9 | 96.8 | 0.117 (0.071, 0.185) |
| 50+ years | 10 | 91.2 | 90.1 | 0.121 (0.085, 0.168) |
|
| ||||
| Criteria of delayed hyponatremia | ||||
| Sodium <135 mmol/L | 22 | 590.4 | 96.4 | 0.104 (0.070, 0.151) |
| Others | 5 | 147.3 | 97.3 | 0.110 (0.045, 0.244) |
eTSS, endoscopic transsphenoidal surgery; mTSS, microscopic transsphenoidal surgery.
A sensitivity analysis.
| Statistics with indicated study removed | |||
|---|---|---|---|
| Study name | Event rate | Lower limit | Upper limit |
| Delayed hyponatremia | |||
| Rajaratnam (2020) | 0.101 | 0.071 | 0.142 |
| Schur (2020) | 0.107 | 0.075 | 0.150 |
| Patel (2019) | 0.103 | 0.072 | 0.147 |
| Younus (2019) | 0.110 | 0.078 | 0.154 |
| Yoon (2019) | 0.102 | 0.071 | 0.144 |
| Tomita (2019) | 0.102 | 0.071 | 0.143 |
| Agam (2018) | 0.109 | 0.077 | 0.152 |
| Bohl (2018) | 0.103 | 0.072 | 0.146 |
| Burke (2018) | 0.108 | 0.076 | 0.152 |
| Deaver (2018) | 0.104 | 0.072 | 0.147 |
| Hollon (2018) | 0.106 | 0.074 | 0.150 |
| Krogh (2018) | 0.111 | 0.078 | 0.154 |
| Bohl (2016) | 0.108 | 0.076 | 0.152 |
| Barber (2014) | 0.105 | 0.073 | 0.149 |
| Yamada (2014) | 0.105 | 0.074 | 0.148 |
| Hussain (2013) | 0.103 | 0.072 | 0.147 |
| Mamelak (2013) | 0.104 | 0.072 | 0.148 |
| Chen (2011) | 0.102 | 0.071 | 0.144 |
| Kinoshita (2011) | 0.100 | 0.070 | 0.141 |
| Zada (2010) | 0.110 | 0.078 | 0.154 |
| Lee (2008) | 0.103 | 0.072 | 0.145 |
| Zada (2007) | 0.101 | 0.071 | 0.141 |
| Sata (2006) | 0.102 | 0.071 | 0.144 |
| Hensen (1999) | 0.111 | 0.080 | 0.153 |
| Kelly (1995) | 0.106 | 0.074 | 0.149 |
| Taylor (1995) | 0.112 | 0.081 | 0.152 |
| Sane (1994) | 0.100 | 0.070 | 0.140 |
|
| |||
| Symptomatic delayed hyponatremia | |||
| Rajaratnam (2020) | 0.050 | 0.035 | 0.070 |
| Patel (2019) | 0.047 | 0.035 | 0.062 |
| Younus (2019) | 0.052 | 0.037 | 0.072 |
| Yoon (2019) | 0.052 | 0.037 | 0.072 |
| Agam (2018) | 0.050 | 0.035 | 0.072 |
| Bohl (2018) | 0.049 | 0.035 | 0.069 |
| Burke (2018) | 0.052 | 0.037 | 0.073 |
| Deaver (2018) | 0.050 | 0.036 | 0.071 |
| Krogh (2018) | 0.050 | 0.036 | 0.071 |
| Bohl (2016) | 0.050 | 0.035 | 0.070 |
| Hussain (2013) | 0.049 | 0.035 | 0.069 |
| Mamelak (2013) | 0.051 | 0.037 | 0.072 |
| Kinoshita (2011) | 0.048 | 0.034 | 0.067 |
| Lee (2008) | 0.049 | 0.035 | 0.069 |
| Zada (2007) | 0.049 | 0.034 | 0.069 |
| Sata (2006) | 0.051 | 0.036 | 0.071 |
| Hensen (1999) | 0.053 | 0.038 | 0.073 |
| Kelly (1995) | 0.049 | 0.035 | 0.069 |
| Taylor (1995) | 0.052 | 0.038 | 0.072 |
| Sane (1994) | 0.046 | 0.034 | 0.063 |
|
| |||
| Cause for unplanned readmission within 30-day | |||
| Patel (2019) | 0.411 | 0.256 | 0.585 |
| Younus (2019) | 0.415 | 0.257 | 0.593 |
| Deaver (2018) | 0.429 | 0.261 | 0.616 |
| Krogh (2018) | 0.491 | 0.340 | 0.644 |
| Bohl (2016) | 0.422 | 0.258 | 0.606 |
| Mamelak (2013) | 0.495 | 0.344 | 0.646 |
Figure 3Funnel plot for verification of publication bias in the present meta-analysis. Egger's test was utilized to verify the presence of publication bias in the meta-analysis for (a) overall incidence of delayed hyponatremia and (b) incidence of symptomatic delayed hyponatremia.
Figure 4Meta-analysis for the associations between delayed hyponatremia and age, gender, tumour type, and tumour size.